Djimila Amimer: [00:00:00] That is into a role model in your organization or within your immediate sphere that you can relate to. You know, tried to find a role model outside of your environment, maybe in another company, maybe in another network. But having a role model does help. If you want to do it, if you really, really want to and you believe in it, you know you will succeed no matter what.

Harpreet Sahota: [00:00:35] What's up, everyone? Welcome to another episode of the Artists of Data Science. Be sure to follow the show on Instagram @theartistsofdatascience and on Twitter @artistsofdata. I'll be sharing awesome tips and wisdom on Data science as well as clips from the show. Join the Free Open Mastermind selection by going to bitly.com/artistsofdatascience. I'll keep you updated on biweekly open office hours. I'll be hosting for the community. I'm your host Harpreet Sahota. Let's ride this beat out into another awesome episode. And don't forget to subscribe. rate and review the show.

Harpreet Sahota: [00:01:21] Our guest today is an experienced business leader and entrepreneur with a broad range of experience across multiple domains, leveraging business strategy for machine learning and A.I. Her efforts have brought clarity to complex concepts and have delivered strongly underpin messages to executives and domains ranging from climate change, risk, energy, business resilience, shipping and trading, supply chain and commercial deals. She has developed frameworks and novel A.I. techniques for investment decisions dealing with uncertainty and project evaluation. She's got a proven track record for leading teams, delivering results and focusing on performance. She's earned a tremendous education in the field of artificial intelligence, which is culminating in her being awarded a PhD in Artificial Intelligence and energy economics from the University of Dundee. She tells her multiple senior level leadership positions and developed a strong business acumen with over two decades of experience in the oil and gas sector, delivering long term strategic initiatives and complex commercial projects across businesses and organizations such as BP and Shell and has since gone on to found MineSenses Global in 2018. Her company, MineSenses Global, is a management consultancy specializing in artificial intelligence with a mission to help businesses and organizations apply A.I. and unlock its full potential. In addition to helping organizations reshape their businesses using A.I., she keeps current on trends by spending a significant amount of time exploring the next wave of A.I..

Harpreet Sahota: [00:02:52] So please help me in welcoming our guests today, A woman who is passionate about A.I. for good and hopes to someday make A.I. accessible to everyone. Dr. Djimila Amimer. Dr Amimer, thank you so much for taking time out of your schedule to be here today. I really, really appreciate you being here.

Djimila Amimer: [00:03:11] Thanks for the invitation and thanks for the wonderful introduction.

Harpreet Sahota: [00:03:15] So talk to us a bit about how you got involved with the field of artificial intelligence, what drew you to the field? And can you talk us from the struggles and challenges you faced while you were on your journey to where you are today?

Djimila Amimer: [00:03:28] Sure. So my journey with a AIs started with the mathematics, as I've done a lot of research in mathematics but around the area of fuzzy logic, genetic algorithms and neural networks and that was like a good kind of starting point in it to basically done do it's artificial intelligence.

Harpreet Sahota: [00:03:48] With all the experience that you've had over the course of your career, where do you see the field of A.I. headed to the next two to five years? What do you think is going to be the next wave of A.I.?

Djimila Amimer: [00:04:00] Oh, wow, that's great And the difficult question to. So there have been ODD, in a couple of weeks in AI and I think the Waverider is the most promising one because there are a lot of people love that I'm aware of AI and data science, we have a lot of businesses coming into stream and, you know, starting to adopt AI but I think the abduction level, it's still low. So there is still a lot basically work needed.

Djimila Amimer: [00:04:33] But I think enough over the next two to five years, we will certainly see an increased number of use cases of AI, whether by businesses or by governments or non-profit organizations. So we will see an expansion of AI across the board and across the economies. I guess with the current pandemic, you know, we are in with the Covid-19 that we have been all impacted with, that it has been a push, you know to the businesses to work towards digitalization and digital transformation and AI will be part of that.

Harpreet Sahota: [00:05:09] Would you mind giving us a quick synopsis of the waves that came before this and how they're different?

Djimila Amimer: [00:05:15] So I guess kind of like the whole AI depending on how you define it, but it's back to the 1950s people starting basically,  having algorithms to try, you know, to teach machines. We have a lot of famous scientists, among them Alan Turing, you know, kind of like today and shook out. A lot of people know about him and the recent movie they did about him. So that's kind of the 1940s to 1950s is kind of the first wave of AI. and that's going to fly. You know, people were starting to do well on A.I., but there'd been a lot of challenges. And then decades later, there's been another wave, much like why kind of the previous waves didn't work is not because of the lack of talent or capability because of what when you look at AI right now and Data science and the machine learning algorithms right now, for example, deep learning, and noter networks, you know, all of the other algorithms. All those algorithms are not too new. So they already existed in the 1940s, in the 1950s and the 1960s. But why kind of like it didn't really have, like a high leap and the high conversion rate into the business life and kind of the normal day life is because at that time of the lack of the computation kind of capacity.

Djimila Amimer: [00:06:43] Nowadays, because we have so much powered computers, we can run those deep learning algorithms, those complex algorithms. And I think kind of like the advance of information technology and the advance of the computer's capability has made kind of small less this wave, a little bit different from the the you know, the all the waves.

Harpreet Sahota: [00:07:07] That's really interesting. Yeah. Because it seems like these methodologies have existed for quite some time it's just we're seeing a resurgence of them because now the technology is caught up and allowed us to, you know, really implement and employ these methodologies in some really meaningful ways. So curious now we're in this next wave, this new wave of A.I. What do you think separates the great Data scientists from the good ones?

Djimila Amimer: [00:07:33] Obviously now with data science becoming an attractive field for a lot of, you know,  young people and younger data scientists. So my tips or my advice to them in terms of how to differentiate yourself and your portfolio and your unique selling requisition.

Djimila Amimer: [00:07:51] We have to be the business domain. So now you can learn kind of like a machine learning. You can learn data science, you can learn kind of like statistics and all those algorithms that are being used. But unless you understand the business context, for example, if you are applying in healthcare, your differentiation would be to know the healthcare business or the healthcare sector. If you're applying in retail or the supply chain, then your differentiation tractor, we'd be to have supply chain experience. So having the business domain will be a big differentiation.

Harpreet Sahota: [00:08:26] What do you think are going to be some of the biggest concerns that a Data scientist will face in the next two to five years?

Djimila Amimer: [00:08:32] So there are different concerns. So let's start with the caveat data because we just touch base on luck, on the you know, the the tip in terms of how you differentiate yourself. I think because there is still a lot of hype around AI. and there's still business who are hiring, a professional so that the scientists don't necessarily appreciate what it is data science will do not necessarily appreciate the benefit overall that data scientist can play in the company.

Djimila Amimer: [00:09:00] So I think from caveat perspective, you have to be very aware before kind of like if you're applying for jobs, you have to be careful in terms of the culture of the company you're joining and whether they have really understood them to have an appreciation for data science otherwise, you might find yourself doing like a done job, just doing some excel, you know, stuff rather than, you know, the attractive data science that were. So that's from caveat perspective. So be very careful when applying for a job that, you know, it's the right environment for you. from kind of like a BA, you know, the other perspective, I think that a number of issues that are on AI that all the scientists and all professional needs to be aware of. One is around AI ethics, the other one is around on a bias. And the other one is, you know, how to make AI transparent and, you know, make it in a responsible way.

Djimila Amimer: [00:09:59] And all these you have to make them into account at the design stage because it's much more efficient and least costly to address this at the beginning rather than address them at the end.

Harpreet Sahota: [00:10:13] I was wondering if you could talk to us about the difference between Neuro AI and General AI and maybe what the future of AI. looks like as we begin to make the shift from narrow to general and, you know, maybe these are some of the ethical concerns that we as Data scientists will face along the way?

Djimila Amimer: [00:10:30] Yes, that's a very important question. We see a lot of questions around  AI and we see a lot of claims of what AI can do. But if you want to categorize them, you would have like three categories. So one is narrow AI, the other one is General AI and then the different one is kind of the supreme part of a AI.

Djimila Amimer: [00:10:53] So let's kind of like try to talk about those three categories. So let's start with the easy ones. So the first category, which is kind of the supreme type of neuro-intelligence, that's the one kind of things you seek in Hollywood movies where you see AI taking over. And, you know, humans being slaves to robots and robots basically, you know, having the power over us. So that's something that's like atleast from a personal point of view is really fictional, and you are like very, very, very far from that. So let's then talk kind of the other two categories. Neuro AI and generic AI. Neuro- AI is the kind of AI have seen so far, so most of the application, whether by big kind of techie companies like, you know, Google, Deep Mind, you know, Amazon, Facebook and so on. They are indeed Neuro- AI. Also kind of application like, you know, from businesses. So, for example, in banking, like a fraud detection or fraud prediction tool that's nodal AI. And what do we mean by nodal AI, it means that it only works within that context. So if, for example, let's take the fraud detection example so that fraud detection, you know, kind of a AI application would be able to predict whether a transaction is going to be fraudulent or not before beforehand, but it only works in that environment.

Djimila Amimer: [00:12:21] So if you take the two out of that environment and you put it in solving another problem, like find to me the the nearest or the closest kind of shop to me, then that tool cannot be used. And none of the intelligence and sort none of the learning of that system could be applied to solve this second question. You can also take it even within the same area, because here in my example, I changed it completely, the application but even you take a chatbot or the intelligent chatbot. The Chat board that has been designed to for a bank, you know, two unsolved banking financial questions like, you know, advising on products wouldn't be a burden. So if you took the same chatbot with that learning you would then be able then to put it in the retail side and then that chatbot will start, you know, answering the retail questions, that that doesn't happen. So this is what we call Neuro- AI it works within a specific environment, within the specific kind of conditions. Now, the general A.I and this is why the most advanced kind of researches and the big techie companies and after and we haven't yet reached that stage is when you take, when would you be able to make an AI use learnings from different contexts and apply them.

Djimila Amimer: [00:13:46] So that's what we call general AI, So AI that can solve different set of problems that can deal with the different complexities and you don't have to do them from scratch because each time they use the learning from one problem and they use that skill to apply for another problem. And that's the kind of intelligence that we are trying to work towards. But again, we are very, very far from reaching and adopt my personal view. And we do some research in this idea in terms of how we can be sure general intelligence in mind, since it's global.

Djimila Amimer: [00:14:19]  In our view, we need four blocks to achieve that. So the first block we call causes reasoning. So I'm sure happy to know that in data science or luck in machine learning AI. Most of the algorithm are based on statistics and correlations so far. So if we take the problem of malaria and fever so the algorithm already know AI or machine learning, we find a correlation between malaria and fever but the tool wouldn't understand that malaria causes fever. So that's what we mean by causes reasoning. So understanding what causes what. In our view, is we need to move to cause of reasoning to achieve general AI and that's only one, one box.

Djimila Amimer: [00:15:05] The second box is around what we call is computational efficiency. So I'm sure, you know, a bug like, for example, the most sophisticated machine learning algorithm so far is deep learning.

Djimila Amimer: [00:15:18] And that's what AI has gain a lot of attraction about. But even within a deep learning, for example, you have to feed it let's say if you would like deep learning to separate cat pictures from dog pictures, you would have to feed that thousands you know, hundreds or thousands of, like, picture of cats and dogs for the machine to be able to know that's a picture that's of dog and that's a cat, whereas kind of if you take a toddler, you know, a human being toddler, you don't need to show a toddler thousands of pictures to be able to distinguish a cat and the dog. So that's what we call a computational efficiency and that's something we would need to achieve in their intelligence.

Djimila Amimer: [00:16:05] The third block and that I'm not going to say a lot about them. The third block, we believe, is about transfer of learning. So how you can transfer learning from a certain problem to very different problem and then the fourth block we call about we need a revolution in machine learning algorithm. So we need a new type, a new breed of algorithm. So we believe, though those four boxes, so causal reasoning, computational efficiency, transfer of learning and then a new breed of algorithms togethe will take us to general intelligence.

Harpreet Sahota: [00:16:43] And that's we are on that path as we move closer to general AI from narrow AI, hat are some of the ethical concerns that Data scientists should keep at the forefront of their mind when they're doing their work?

Djimila Amimer: [00:16:56] So that's a very important and different question because we see as AI stop expanding, the issue with the expansion is it's going much quicker than compacter regulations from governments and institutions. So this is why that is a goal. So it's very important to ensure scientists or AI research in developing a tool or, you know, an application to consider ethics and bias at the early stage of design. So making sure that issues around, you know, data privacy, tracking of data, it's handled within a certain framework and you have like a certain key principles you are following. And her in Europe, you know, we have the GDPR. So there like a certain kind of regulations from the European Commission and I'm sure that, like, you know, equivalent to regulations in the US and Canada and do you know some other countries that you need to adhere to in terms of how you had the Data. But it's also around your attitude has to be if it comes so it has to cause any harm or you know any prejudice, you know, to a certain like group, ethnic minority or it has like anythin preposterous  but you also need to make sure that you understand your Data, you understand that there isn't a bias in your Data.

Djimila Amimer: [00:18:16] So there is a very famous example that is used in terms of a lack of, you know, handling AI bias in Data so that a lot of examples we can mention Amazon recruitment tool. So Amazon bank, I think if like two years ago or maybe 18 months ago, they developed a recruitment tool but then after kind of like they have found out that the tool was biased towards women, because when you send like two CV with the same type of, you know, experience and skills, but then with the different gender, one is male and the other one is female then the tool was given preference to the male gender application for a technical goals. So then they found out that obviously the tool was biased towards women for technical goals, and then Amazon decided basically to scrap that tool but going back quite, there was an issue with that. So you have to be careful. So, for example, if you are developing an application here, the example was in recruitment, you have to understand your Data, so you have to understand, for example, in the 90s, if we got to the early kind of like like 19th century, so like the 1910s or the 1920s or the 1930s, if you look at certain jobs. So, for example, if you look at health care, you might find that maybe in the 1920s there were more men doctors compared to women, doctor.

Djimila Amimer: [00:19:49] and that worked the opposite. There were more women nurses compared to men nurses. The same thing. you may find like in the same kind of Era, if you take technical job, like in the oil and gas industry, you know, like a heavy kind of like or mining industry, you know, heavy kind of industry, you would find that's like in that era, you would find kind of like there would have been more me doing those hard technical roll compared to women but obviously that timing is not  anymore reflection in the society and the fun we live right now. So you cannot take that historical data and then make a conclusion and prediction without acknowledging that there would have been a bias in that data, because by nature, they would there would be more men Doctor, compared to women doctor in that era. And you need to correct for that. So this is what we need, you need as it data scientist you need to be aware whether your data could have been bias and if you think that's the timing  of the historical data that doesn't reflect anymore the present or the future time, then you need to make some corrections before starting predicting and using the tools.

Harpreet Sahota: [00:21:00] I always like to say that it might be the machine that's learning, but it is the human that is teaching it. So we still need to be aware. I think what it is that we're doing.

Djimila Amimer: [00:21:09]  Absolutely. It's learning and it's being trained on a certain set. So if you feed that that that that is bias, then the learning will be AIs will be biased.

Harpreet Sahota: [00:21:19] How can AI Be used to help us fight this Covid-19 pandemic?

Djimila Amimer: [00:21:24] Yes. So we have already seen that AI has been developed in different ways to help with the Covid-19. I'd just give like a few example. So obviously, for AI data science audience, they would know that most of the successes that AI had so far is in deep learning. And the good news about deep learning is, is very, very, very good in terms of accuracy, right? When it comes to image recognition.

Djimila Amimer: [00:21:56] It's no surprise that the first application of AI to help in the Covid-19 was in the application of taking the scans of the lungs. Patients that had already ill by Covid-19 or that suspected, so that, you know, they take like scans of the lungs and then they use then AI deep learning that is very good in terms of imaging condition to try to recognize whether they lack some particles, whether their lung that is the virus inside the lungs. So that's one of the application of AI.

Djimila Amimer: [00:22:36] And that has been complimentary to the other tests. You know, for example, a blood test that, you know, is used to order sort of another blood test, you know, kind of like the would they put like something on the off the back of the nose. So to get like a sample and then do a test to see whether you have Covid-19, scanning lungs was one wager, you know, using AI to fight Covid-19 So that stands in terms of detection.

Djimila Amimer: [00:23:06] Another application which is not above detection. So once the patient, you know, they have already  you know that what he did test positive. So we know that the patient has caught Covid-19. Obviously, we know that, like the way Covid-19 has impacted people that have impacted the brain in very different ways. So there are like people who got like very, very sick and they like, you know,  people who got lucky and got away wel  with it, just like very, very minor symptoms. So if a patient could like the virus, then it's just very, very helpful for the health care sector to know in terms of triad, you know, who is the patient, who would need most of the help? So using data  from kind of what we know so far from Covid-19 to predict which patient is likely to have the most severe symptoms so they can have priority in terms of treating the disease at the earliest stage rather than leaving it too late. So those one like two type of application in terms of detection and triad in terms of, you know, who should get the first treatment or who should get the first priority using AI.

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Harpreet Sahota: [00:24:57] Do you think that we could use AIand  machine learning to identify or at least predict the next pandemic?

Djimila Amimer: [00:25:05] We could. So it hasn't been done so far. Maybe that's kind of something, you know, once we get, you know, over most of the Covid-19, you know, we can use kind of in terms of predicting the next pandemic but I differently believe that like this kind of scenario, you could use a AIs to kind of like, you know, predict or, you know, kind of like shape. So some of them.

Harpreet Sahota: [00:25:30] So you've done some amazing research in your career. Which one of your works do you think is most relevant to our current times and can you maybe make the connection for us?

Djimila Amimer: [00:25:42] So I've done a lot of research. But like most of my research has been like in a business context. So we haven't gone through the introduction of mind senses global, but so funded mindsense global around like 2 years ago to help businesses and organization apply AI. Before that, I worked in the energy sector. So one we see, I had a lot of opportunity to apply AI and machine learning to my career in the energy sector but we wouldn't just call it AI, we would just call it problem-solving. So one that I think is most relevant to our current times, I have done a lot of work in climate change. Climate change is a very relevant topic, an agenda for us and I think what this pandemic has shown so obviously because of the pandemic, there's been a decrease in industrial activity, a decrease in transport, in human activity. So we could see that obviously CO2 emissions have decreased during this time, but it's the short to temporary decrease because we know it's going to catch up as soon as you know things and you know our activity and will start but the climate change agenda is saying.

Harpreet Sahota: [00:27:02]  I'd love to get more into the work that you've done at mind sense global, can you talk about some of the projects that you're working on or some of the initiatives that you're undertaking?

Djimila Amimer: [00:27:10] I'm very passionate about AI and kind of one of my mission is to make AI available to everyone. So and this is mainly the main reason why I set it up, Mind senses Global back in early 2018. The overall vision is to help businesses and organization apply AI. The way we apply it is two fold, different services. So one is around AI education and because of my question in a AI, so I do a lot of activities under AI education. So we will run massive classes, workshops around AI, you know demystifying AI, I mean bringing kind of like the business perspective. We show kind of how AI has been used in several sectors, you know, how it has been used in healthcare, in detail in our energy sector you know across the board. I do myself a lot of like a podcasts and webinnars, you know, to help, you know, bring the awareness and fight to the myths and the hype, you know, the myths and the hype around AI.

Djimila Amimer: [00:28:15] The second service is what we call a AI strategy because once people this is not about AI in the natural step for them is for them to want to apply it but then most of them don't know where to start. So this is where kind of we had them develop an AI roadmap. So we talked them through the key pillars behind and a strategy. So what kind of things I'd be needing to have to be able to try to know our business decisions if they like and I'm sure there may be several ways on how to apply  AI, which one will add the most value to that business. So this is all the AI strategy. 

Djimila Amimer: [00:29:01] The third services around a tooth's and solutions. So that's the most tricky bit of the offering. This is where of like we go and, you know, we develop, you know, the application for the businesses but the way we see it kind of like is slightly different. So we see a AI as a means to an end. So we see a as a tool. So we always start with the problem. We never start with AI, I go to a lot of conferences and meet a lot of people and you know potential clients. And then when they ask them  have you applied AI? They say, yeah, yeah, we got this tool and decided not to name , but damn it, did I you know, we got lucky this late tool and so on.

Djimila Amimer: [00:29:47] But then the next question when you ask. Okay, good. What are you doing about this? And then this is what they get stuck us. All we got. No, we just got there. You know, we're still thinking about that. And for us, this is the wrong way to go about it. I think if you're out of business, you should start with your business contacts. You should start with your problem. And then along the way, or framing good problem and trying to find a solution to a problem, then we have to find a way to use AIs to solve that problem. So that's what the goals under AI tools.

Djimila Amimer: [00:30:20] And then the last service we offer is the ground investments. So that's going back to the AI app. The AI hype has caused a lot of confusion to people especially to AI investors. I'm not talking about like the big big VC stuff that may have their own big internet teams that can do proper due diligence on startups. But look, the smaller firms and like the family office and the entry investors, they're really struggling when it comes to investing in startups, let me mention one of the statistics to show the hype.

Djimila Amimer: [00:30:59] So there has been a survey done by AMC Venture. So that's a European survey. So they surveyed European AI startups and the survey results showed that a big percentage, which is around 40 percent. So 40 percent of European AI startups do not actually use AI. So that's a big issue when it comes to tto the AI hype and the AI investment. So what do we do under this service, so we help AI investors doing proper due diligence on AI startups before they invest. We advise them on AI firm on the most promising area and so on. So those are the four services, AI Education, AI strategy, AI tools and AI investments.

Djimila Amimer: [00:31:48]  In terms of the clients and  the sectors, I have deliberately make the decision when I set up the company earlier to make it open. So we didn't say we will only do the energy sector, we would only duty time. So we have clients across the board and across sectors. Obviously, we have priorities that much our skills like healthcare and supply chain and retail and maritime shipping and energy but apart from from that  we help people across our clients, across different sectors.

Harpreet Sahota: [00:32:28] I was wondering, I could prick your brain about entrepreneurship. So do you have any tips or words of encouragement or advice for someone who's probably been toying with the idea of entrepreneurship but just hasn't gone and made the leap?

Djimila Amimer: [00:32:42] Yeah. That's a good question. So kind of like specially for me, because before being an entrepreneur myself, you know, I came from the corporate, you know, the safe corporate environment. So I was in corporate I like, you know, a senior role in a corporate organization. So I think of, my advice, if you have an idea and you want the kind of like a person who is keen into entrepreneurship, go for it, but go for it apply with a wide open eyes. So be aware that's kind of your journey is going to be lonely. So you have to have like, a lot of resilience to be able to sustain yourself and you grow your business, but also kind of like don't be shy at all for, you know, sharing your ideas and going out and knocking on doors, because that's how kind of like, you know, you need to get like networking and you need to kind of like to build on your exposure and visibility.

Harpreet Sahota: [00:33:44] So what are some key traits or qualities that a person should possess, if they wanted to become a full fledged successful entrepreneur and how could they cultivate these traits and qualities within themselves?

Djimila Amimer: [00:33:57] It's difficult because I don't that is like a single trait. You know, it's kind of for me, I believe that if you want to do it, if you really, really want to and you believe in it, you know you will succeed no matter what, you know, what kind of trait or your kind of profile. But I think that obviously I like some attributes that are positive and that helpful if you have them. One, I think it's still my number one is, you have to be resilient because along the way you will go through a lot of rejections before you get like, you know, your first success. You get luck. A lot of luck, small success before you get, like, your big kind of like, you know, major, great success. So you have to be resilient. But at the same time, you have to  be able to adapt to a changing world. So, you knowif you are an entrepreneur and go in there, you have to be able to test different strategies and different offerings. So if you go out and you know you are pitching in a certain way or you're not offering a certain product and it doesn't go, it doesn't go well or, you know, you haven't received like, you know, welcome reception then along the way, you have to tweak, You have to change that strategy. So maybe you tweak your offering or you may tweak your product or you may tweak your business model, but you have to change. You can not keep having the same pitch again and again and again and feeling again. So you have to be able to adapt to a changing environment.

Harpreet Sahota: [00:35:32] So in terms of Data science and AI entrepreneurship in this Covid world, what are some problems worth tackling that an enterprising Data scientist can identify and maybe turn into an opportunity?

Djimila Amimer: [00:35:47] That's a good question, because I think what has kind of Covid-19, but obviously from kind of on a lot of suffering, you know, that and the negative impact globally that has such a humanity, I think it's kind off if there is any positive.

Djimila Amimer: [00:36:06] And I'd like to say it is a positive event because. No, you know, if such a such, this one is a positive. I think if we have to find like a positive lens into it, I think it will looks like it will have you know, this event has pushed us to see things in a different way. First of all, realize that,  having a rethink of our values and principles and realize and reaise that our time is short and, you know, we have to really prioritize, you know, things.

Djimila Amimer: [00:36:37] So from a business perspective, I think we will see going ahead that there would be maybe a lot of requests for data scientists and AI for automation.So kind of like, how can we make the values procedures, you know happening procedures that are doing and are being done by human beings right now. How can we automate them so we can then free our self and for much higher priority tasks where we need, like, you know, to use our intelligence in it. So there would be a lot of requests for the automation. But again, because kind of like, you know, off the you know, the economic impact of Covid-19. So, there will be a more push forhow to improve the business performance and the way we view within mind senses. You know, when you look at the business performance using AI., so you could either use AI to reduce your costs or use AI to gain competitive advantage or you can use it to improve your customer satisfaction. You know, for the customer services. So there would be still a lot of different ways on how AI could help make businesses more efficient in the New World after Covid-19.

Harpreet Sahota: [00:38:05] Thank you for that insight, I really appreciate it. So, you know, a lot of up and coming data scientist, they tend to focus primarily on hard technical skills and they think that that's what's going to separate them from the rest of the crowd, from the rest of their competition when applying for jobs and what not. What are some soft skills that candidates are missing that are really going to separate them from their competition?

Djimila Amimer: [00:38:27] Yeah. Again, that's another good question. So we see it a lot in the tech area or kind of like, you know, the new joined us where they say kind of let's learn every single open source tool or, algorithm or application and they think it's by knowing to code in Python and all and you know, all those software sort, you know, kind of like coding programs is enough.

Djimila Amimer: [00:39:02] We see there are a certain minimum requirement as a data scientist. You are supposed to know kind of like how to code and you kind of how to handle data and so on. But I think the most critical, you know, going back to your previous question in terms of how you differentiate yourself, you know, it has to come to the soft skills, not anymore of the hard skill, because you have to take as a given that everyone, if you are a scientist or the older data scientist trying to apply for your job or trying to do you know similar projects to you, we had very similar techie skills to you. So the only way to differentiate yourself would have to be through the soft skills, not the hard ones and the soft skills for me, what has been testable for me is you have to have like two soft skills.

Djimila Amimer: [00:39:56] One is around clarity of thoughts.So how can you make very complex concepts when dealing with AI and machine learning and that's all,, coding in a business, how you can take a  very complex concepts and turn them into very simple messages to your crowd, to your audience. So it's the clarity of thought and it's the simplicity of actually learning than the messages.So that's kind of like the top skill for me.

Djimila Amimer: [00:40:28] And then the second top skill in terms of the soft skills would have to be in terms of kind of influencing skills because don't forget as a data scientist, whether you are a member, whether you are the lead and you have a team in a business context, you have to deal with all those. You have to deal if you are a manager, you have to deal with your junior data scientists. If you are data scientist, you have to deal with kind of the way that team, you have to convince kind of senior business management, it terms of your idea on your projects. So you need to know how to influence. You have to negotiate kind of like how to bring your idea into the table. So I think those are the two top sub skill for me.

Harpreet Sahota: [00:41:13] Thank you. So we got a lot of listeners and a lot of data scientists out there who are up and coming Data scientists, maybe their students, maybe they're having a hard time getting a job, but eager to put their skill set to the youth. How can a student with nothing but a laptop and an Internet connection to use AI for good?

Djimila Amimer: [00:41:36] Yeah. Thanks. So there are a lot of opportunity for people coming into this area to contribute. So in wider terms. So before going into the specific kind of the AI for good, because that's another topic on its own. But if you're coming to this area and, you know, I think the quickest way to learn and you have to put your knowledge into practice so, you know, kind of like a lot of platforms like a getapp and, you know, cargo where he can go and see but like what all the people are working on, you can go into the the open source platform and get kind of the the latest, you know, kind of a code and the latest projects and try to contribute specifically when talking about AI for good obviously there are a lot of projects that help in the area of AI for good.so AI has been used to predict, for example, earthquakes. It has been used to predict in terms of the loss of forestation. It has been used in terms of mental health. So there are a lot of topics, AI for good going right now, but there are still a lot of organization, kind of like helping in this area. So I think the latest one that has come across is an organization called, I think it's called Omnida

Djimila Amimer: [00:43:06] And what they do. I can send you afterwards i have this kind of links that that maybe you can kind of add so people can look. So what they do. So it's the kind of organization that brings together different professionals including kind of upcoming, kind of like a junior data scientist, people  new into kind of this area and they get together to try to solve and offer good projects. So I think kind of like each maybe each month, that is a very different topic. So  they've done I think earthquakes, climate change they've done is kind of like how harrasment. They have done like a lot of topics so you could kind of like, you know, kind of like participate to have this kind of things and this is only one example that can a lot of organization and networks,organizing, either hacket tons  organizing in all projects, work, you know, for AI feel good.

Harpreet Sahota: [00:44:08] I'll definitely be shooting for the link today  in the show notes. Do you mind spelling that out for us? Was it Omnida?

Djimila Amimer: [00:44:15] Yeah, I think so. I'm not sure. So I think it's O, M, N, E, D, A but I'll make sure I get to correct for you on the notes.

Harpreet Sahota: [00:44:22] I was wondering if you could speak to your experience being a woman in tech, being a woman in STEM and if you have any advice or words of encouragement for our women listeners out there?

Djimila Amimer: [00:44:34]  The technology sector is like a sector where so far we see less women than men and I'm hoping that, like in the future, it will change. But that's no different from the previous sector that I was in, the oil and gas and the energy sector, I was also in a sector where there are typically you will find maybe more men compared to women actually in the senior role in a work position. So it's always challenging whether it looks to us or just for women, because I think the question is still up front it you know, if you belong to any type of category, where do you find yourself as a minority in a wider environment, I think that's a relevant question to you. So kind of like first of all, it's kind of like don't be disheartened. Don't say so I know because I'm the only one. It means, you know I have no role model and I can never go out because, all the people up like that are different from me.

Djimila Amimer: [00:45:34] So I think one is to be resilient. But the other one is kind of like to try to find a role model. So if there isn't a role model in your organization or within your immediate sphere that you can relate to, you know try to find a role model outside of your environment, maybe in another company, maybe in another network. But having a role model that's hard. And the other one, you know, going back to, you know, being resilient You know, you have to be resilient. You have to take it on the chin and, you know, persevere. And by showing good capabilities, you will go through.

Harpreet Sahota: [00:46:11] So what can be Data community do to foster the inclusion of women in STEM and will help them along the way?

Djimila Amimer: [00:46:20] Yeah. So that's a good question, because we we are all responsible. So we all have to take actions to help this wider issue. I think kind of having role models is very, very important, especially if you are dealing with younger generation or young adults, even like pupils, at schools, unless they know a certain person. So you find a female, unless they know another female doing another Techie job or doing another specific job, maybe that job or that title or that type of kind of activity wouldn't even come to their mind. So I think it's very important to have role models so kind of I think for data communities and for all of us involved, we have to play our part. So kind of like be the role model. So, like to be a mentor for otheer people, don't don't wait to be asked to be a Bon-Ton. If you know some people in your environment you think you can help, offer the help, don't wait to be asked.

Djimila Amimer: [00:47:31] But also we need visibility. So this is why I do a lot of podcast, webinars, speak at conferences, presentations, because the more we see diverse kind of like faces and divers kind of like talents in AI, the more kind of like you we will be more inclusive.

Harpreet Sahota: [00:47:58] What's up, artists? Be sure to join the free, open, Mastermind slack community by going to bitly.com/artistsofdatascience.It's a great environment for us to talk all things Data science, to learn together, to grow together and I'll also keep you updated on the open biweekly office hours that I'll be hosting for our community. Check out the show on Instagram at @Artistsofdatascience. Follow us on Twitter at @ArtistsOfData. Look forward to seeing you all there.

Harpreet Sahota: [00:48:27] The last question before we jump between lightning round here. What's the one thing you want people to learn from your story?

Djimila Amimer: [00:48:34] Well, I'm not sure, but I think kind of going back. So if you look into my journey and story, you will see that even though there are a lot of common traits around that you can see that I have change environments and they have changed businesses and rules. So I think one of the key bit is to be able to adapt to change. So I think the more you adapt and the more you change, the more skill and that's what has helped me so far.  I wouldn't have accumulated the capabilities such that they have so far if I haven't gone through all those changes.

Harpreet Sahota: [00:49:12] Just go ahead and jump into a lightning round here. What if your Data science superpower?

Djimila Amimer: [00:49:17]  Oh, dear.I'm not going to be claiming to be super powerful. But again, I think for me and for a Data scientist, I think what has helped me the most is simplicity, you know to be able to do things in a very clear, simple way.

Harpreet Sahota: [00:49:39] What do you believe that other people think is crazy?

Djimila Amimer: [00:49:43] I think that everything is possible.

Harpreet Sahota: [00:49:46] If you can have a billboard anywhere, what would you put on it and why?

Djimila Amimer: [00:49:53] I'm very fascinated by a kind of like the universe and the different planets and obviouly you know, with the latest kind of like achievement, you know the launch with the USA in a rocket with the space, I'm very interested by Mars. So I'll definitely put, like, the Mars planets in my board.

Harpreet Sahota: [00:50:19] So what's an academic topic or just an area of research outside of Data science that you think every data scientist should spend some time studying?

Djimila Amimer: [00:50:30] It's not this is differently outside. I think, you know for AI and data science you will need the solid background of mathematics. So I think for  mathematics is number one topic.

Harpreet Sahota: [00:50:46] What's the number one book, fiction or nonfiction or if you want to pick one from each, that's OK that you would recommend our audience read and your most impactful take away from it?

Djimila Amimer: [00:50:59] So a book that, you know, I'm very, very fond of. So I don't recollect the exact title, but I can kind of like more or less kind of translated but I'm like very keen in terms of, you know, kind of, you know, action and intention. So that is a book by Dr. Wayne Wire, who is like a very famous you know, he has passed away, I think, like a year to go. But he is has been very, very famous indeed in the area of personal development. So the paraphrase kind of like title says something around, "if you change the way you see things then things when they start changing" and it's all about countered the power of intention. So for me, that's like a very relevant book because like I think action oriented, you know, focus is very, very crucial to succeed in this life.

Harpreet Sahota: [00:51:55] Definately, I'll try to find title for that and then I'll try to cross-reference that with you to make sure we are on thesame note.

Djimila Amimer: [00:52:00]  I send you. I send you on the note.

Harpreet Sahota: [00:52:02] Definitely. So if we could somehow get a magical telephone that allowed you to contact the 20 year old  Djimila, what advice will you give her? First tell us,  at 20 years old. Where were you at? What were you doing? And what advice would you give her at that time?

Djimila Amimer: [00:52:18] So 20 years old, although I was still studying, I was up to university. And I think kind of that age I was just about so, I was heavily involved in mathematics. And that was just about starting getting involved with neural networks center and fuzzy logic. So I think it was just about I think for me it was between 20 and the age of 21 where I started to go getting into AI.

Djimila Amimer: [00:52:46] In terms of the advice, I think it wouldn't be kind of like a career type of  advice, but I think it is more kind of a lifestyle advice. I would just tell myself, you know, just like take it easy and chill out.

Harpreet Sahota: [00:53:02] So what is the best advice that you've ever received?

Djimila Amimer: [00:53:05] When I was in the business, in the corporate. So the best advice i received then is around, how  would tailored your message to your different audience? So how you speak to techie people in a techie language and how you speak to the general public in kind of like general reach and  like, you know, somebody in the middle for business. So it's all about how you tailored your message to fit your audience and that has been very useful for me so far.

Harpreet Sahota: [00:53:34] What motivates you?

Djimila Amimer: [00:53:36] I love solving problems, especially if they are complex and challenging. I love, I love, the challenge.

Harpreet Sahota: [00:53:42] What's the song that you currently have on repeat?

Djimila Amimer: [00:53:45] The song on repeat? It's been  a while, I haven't listened to any music recently but I think I was just like kind of like soft classical music that you can just put in the background and then, you know, go on with the other current activities.

Harpreet Sahota: [00:54:05] So how can people connect with you? Where can they find you online?

Djimila Amimer: [00:54:09] Yeah, sure.I am very happy for people to reach out, so they can either reach out to me through LinkedIn, they can find me in LinkedIn at Dr. Djimila Amimer or they can contact me through my company's LinkedIn page in Mindsense global or they can just go to my company web site www.mindsenses.co.uk

Harpreet Sahota: [00:54:37] Dr. Amimer, thank you so much for taking time to schedule to be here today. I really, really appreciate you coming on to the show.

Djimila Amimer: [00:54:44] Thank you so much. Pleasure to be here.